A multiple access wireless communication system enables multiple users to simultaneously access and use the system. These systems employ multiple access schemes, such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA). For FDMA, the multiple access wireless communication system's spectrum is divided into fixed frequency slots and users are allocated one or more slots for communication.
For CDMA, the multiple access wireless communication system assigns a unique code to each user that is used to modulate a signal from the user and that modulation identifies the user to the system. In CDMA systems, all users transmit and receive code-modulated data simultaneously, and the data for a given user is distinguished at the receiver using the code assigned to that user.
For TDMA, the multiple access wireless communication system divides time into fixed slots and users are allocated one or more time slots for communication over the system. Additionally, Global System for Mobile communications (GSM), is another type of multiple access wireless communication system that employs narrow band TDMA to allow at least eight simultaneous telephone calls on the same radio frequency.
Typically, a forward communication channel is used to transmit data signals from a base station to a mobile station; and a reverse communication channel is employed to transmit data signals from the mobile station to the base station. However, the transmission of a signal through a wireless medium can have arbitrary attenuation and delay due to hilly terrain, atmospheric conditions, outages and other factors. Also, the Doppler frequency shift in wireless signals transmitting from a fast moving vehicle can create fast fading channels where channel parameters can vary within each received block of data. To compensate for these less than ideal conditions, a base station's detector will employ various methods to estimate the attenuation and delay introduced in communication channel so that it can detect and identify each user's signal in the received signals.
For example, TDMA wireless communication systems often employ a training sequence that is included in each transmitted block of data to estimate channel. These methods usually employ an inverse of a matrix formed from the training sequence data. Also, since the training sequence can be known in advance, the inverse is typically precalculated and not separately performed for each received block of data. Similarly, CDMA wireless communication systems employ substantially the same methods based on a pilot signal that is included in each block of data.
Recently it has been shown that schemes exploiting several uncorrelated transmitting antennas are very promising for wireless communications. For example, delay diversity transmission (DDT), the simplest transmit diversity scheme in which delayed copies of the modulated signal are transmitted from multiple antennas, can significantly improve the performance of receiving devices (detectors). However, the transmit diversity gain is obtained by artificially introduced multipath requiring more channel parameters to be estimated given the same training sequence. Hence, the transmit diversity gain may not be fully utilized due to degradation of channel estimation accuracy. For example, accuracy of channel estimates based only on GSM training sequences (originally designed for channels without transmit diversity) may be not enough for transmit diversity schemes that in turn deteriorates the receiver performance.
The receiver performance may be improved by utilizing semi-blind detection methods, which combine information obtained from the known training sequence and unknown data to improve channel estimates. In particular, data-aided iterative channel estimation (ICE) or joint channel estimation and symbol detection based on the Expectation-Maximization (EM)-algorithm along with other iterative methods may be used. However, application of these methods implies a complicated inverse of a matrix formed by all transmitted data in a block. Since this matrix includes random transmitted together with known training sequence, then computational demanding matrix inverse should be performed for each transmitted block. Thus, there is a need for a low complexity matrix inverse that may be embedded into the EM (or some other iterative process) based receiver.